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Prediction of Domestic Wastewater Discharge in Urban Towns of Pearl River Based on ARIMA 9th

Prediction of Domestic Wastewater Discharge in Urban Towns o...

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9th International conference on Advances in Energy Resources and Environment Engineering, ICAESEE 2023

作者： Liu, Lidong He, Yantao Liu, Sijia Liu, Suping Luo, Yongbin Mo, Jiayu Liu, Chunzhen Department of Computer Science Guangdong University of Science and Technology Dongguan523668 China School of Mechanical Engineering Hunan University of Science and Technology Xiangtan411201 China School of Teacher Education Shanwei Institute of Technology Shanwei516600 China

ISBN: (纸本)9783031665042

the discharge of domestic sewage by urban residents has always been a major factor causing water pollution in rivers, and with the continuous improvement of people's living standards, the amount of domestic sewage discharge is also increasing year by year. In order to predict the specific situation of domestic sewage discharge by urban residents in the Pearl River area in the future, this paper establishes an ARIMA model, combined with the existing 21 year domestic sewage discharge data, to make predictions for the next 10 years. During the prediction process, the original data is subjected to ADF test to obtain the optimal difference data, and this data is subjected to ACF and PACF analysis, and the model test table is drawn. the white noise sequence of the data was examined using both ACF and PACF. the analysis concluded that both AR residuals and MA residuals are sequences of white noise, thereby meeting the prerequisites for the ARIMA model. Subsequently, the formula for the ARIMA model was derived, and a model diagram of the time prediction sequence was illustrated. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： River pollution

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Boundary Extraction for Unseen Entities Prediction Using Bidirectional Long Short-Term Memory 17th

Boundary Extraction for Unseen Entities Prediction Using Bid...

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17th International conference on Multi-disciplinary Trends in Artificial Intelligence, MIWAI 2024

作者： Kuntichod, Watcharet Lee, Wilaiporn Prayote, Akara Department of Computer and Information Science King Mongkut’s University of Technology North Bangkok Bangkok10800 Thailand Department of Electrical and Computer Engineering Faculty of Engineering King Mongkut’s University of Technology North Bangkok Bangkok10800 Thailand

ISBN: (纸本)9789819606917

Named Entities Recognition (NER), which is a task to identify and classify named entities within text, has gained significant popularity in recent years. this task often requires pre-labeled data and large datasets to achieve high accuracy. A key challenge in NER is extracting unseen entities that have not been previously labeled or are new terms, posing difficulties in adapting to rapidly changing data and the emergence of new vocabularies. In this article, we propose a model utilizing Bidirectional Long Short-Term Memory (Bi-LSTM) for boundary extraction. the model aims to enhance the detection and labeling of unseen entities by leveraging Bi-LSTM's strengths in capturing contextual information and accurately identifying entities boundaries. the model demonstrates high potential in identifying unseen names without pre-labeling. Our study demonstrates the model's showcasing its effectiveness in discovering unlabeled words and unseen entities in thai language datasets. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Labeled data

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Local and Global Features Fusion for No-Reference Quality Assessment of Super-Resolution Images 27th

Local and Global Features Fusion for No-Reference Quality ...

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27th International conference on Pattern Recognition, ICPR 2024

作者： Liu, Yun Tang, Tong Zhu, Zhiyuan Ying, Jun School of Communications and Information Engineering Chongqing University of Posts and Telecommunications Chongqing China Advanced Network and Intelligent Interconnection Technology Key Laboratory of Chongqing Education Commission of China Chongqing China Chongqing Key Laboratory of Ubiquitous Sensing and Networking Chongqing China

ISBN: (纸本)9783031784972

Image super-resolution (SR) technology aims to enhance the resolution and improve the quality of images, and it has been widely used in face recognition, small target detection, medical imaging and remote sensing image analysis. Image quality assessment (IQA) is important for optimizing SR algorithms. At present, the main challenge lies in how to comprehensively learn features to characterize perceptual properties of human visual characteristics. therefore, in this paper, we propose a no-reference quality assessment method for SR images based on local and global features fusion. First, a two-branch feature extractor is proposed, which uses convolutional neural network and vision transformer respectively to extract local features and global features. then, considering the perception properties of the human visual system (HVS), local and global features are fused and adaptive weight strategy is applied to predict the quality score. Finally, Experimental results show that the proposed method outperforms the state-of-the-art methods in terms of prediction accuracy and generalization capability on benchmark datasets. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Convolutional neural networks

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PBL Meets AI: Innovating Assessment in Higher education

PBL Meets AI: Innovating Assessment in Higher Education

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17th International conference on computer Supported education, CSEDU 2025

作者： Divjak, Blaženka Svetec, Barbi Aničić, Katarina Pažur University of Zagreb Faculty of Organization and Informatics Varaždin Croatia

ISBN: (纸本)9789897587467

Problem-based and project-based learning (PBL) is a creative, inquiry-based process, enriched by teamwork, implemented through a longer timespan, with considerable student workload. As the process is normally conducted with a high level of student autonomy, there is often no way of monitoring what resources students rely on. In this context, the rapid availability of generative AI can support not only innovation, but also unfair practices. In this article, we will present case study research on the use of AI in PBL summative assessment in higher education, based on six courses. We analyzed and compared six courses of different levels of study (undergraduate, graduate, postgraduate), in different areas/subjects (mathematics, IT, project management, education). In some courses, it was obligatory to use AI, while in others it was either optional or not foreseen. We analyzed assessment results, student surveys and teacher testimonials, using mixed-method research. Based on the research cases, we identified three possible models of integrating AI in PBL and provided recommendations. Copyright © 2025 by SCITEPRESS - science and technology Publications, Lda.

关键词： Project management

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Sentiment classification of MOOC courses by merging local context focus and Bi-directional gated recurrent unit 4

Sentiment classification of MOOC courses by merging local co...

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4th International conference on computer Vision, Application, and Algorithm, CVAA 2024

作者： Jin, Jiayong Dong, Yongquan He, Bugui Zhou, Nan Yan, Linke School of Computer Science and Technology Jiangsu Normal University Jiangsu Xuzhou221116 China Xuzhou Cloud Computing Engineering Technology Research Center Jiangsu Xuzhou221116 China Jiangsu Engineering Technology Research Center of ICT in Education Jiangsu Xuzhou221116 China

ISBN: (数字)9781510687622

ISBN: (纸本)9781510687615

Existing MOOC review sentiment classification methods do not fully utilize the local context information associated with aspect, and they ignore the connection between local and global contexts, while resulting in modeled features that lack the information connection between aspect and contexts. In this paper, we propose a model that incorporates Local Context Focus (LCF) and Bi-Directional Gated Recurrent Unit (Bi-GRU). First, the BERT model is used to dynamically encode course reviews. then, global semantic features are extracted using the Bi-GRU model to strengthen the connection between the preceding and following texts. then, the LCF model based on multi-head self-attention is used to obtain local contextual features and splice them with global semantic features. Finally, the Softmax function is utilized to output the classification results. the experimental accuracies of the proposed model on the three MOOC course review datasets reach 97.96%, 96.76%, and 94.16%, respectively, which are improved by 0.70%, 0.42%, and 0.03% over the suboptimal baseline model. the proposed model significantly improves the effectiveness of MOOC course review sentiment classification, and provides a useful reference for the optimization and improvement of MOOC courses. © 2025 SPIE.

关键词： Semantics

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LCRNet: Unsupervised Non-Rigid Point Cloud Registration Network Based on Local Correspondence Relationships 16

LCRNet: Unsupervised Non-Rigid Point Cloud Registration Netw...

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2024 16th International conference on Graphics and Image Processing, ICGIP 2024

作者： Liu, Lixiang Zhang, Tiantian Yang, Boxiong Hu, Ronglei Wu, Yiqi Chen, Yong Li, Yanli School of Information & Intelligence Engineering University of Sanya Sanya China School of Computer Science China University of Geosciences Wuhan China Co. Ltd Zhejiang Ningbo315336 China Department of Information Engineering Ordos Vocational College Ordos China

ISBN: (数字)9781510688780

ISBN: (纸本)9781510688773

Currently, the registration of non-rigid 3D objects remains a challenging task. this paper proposes an unsupervised non-rigid point cloud registration network based on local correspondence relationships, namely the LCRNet. By learning the partial matching matrix to obtain more accurate point cloud correspondences and optimizing the matrix in combination with the Sinkhorn algorithm, we successfully eliminate the noise and outliers in the initial matching. In addition, through the adaptive learning of the transformer network and using complex geometric properties, we realize the deformation of non-rigid point clouds. the experiments on multiple datasets indicate that the LCRNet achieves accurate unsupervised non-rigid object registration, outperforming comparative methods. © 2025 SPIE.

关键词： Matrix algebra

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On-Your Marks, Ready? Exploring the User Experience of a VR Application for Runners with Cognitive-Behavioral Influences

On-Your Marks, Ready? Exploring the User Experience of a VR ...

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17th International conference on computer Supported education, CSEDU 2025

作者： Cardenas Hernandez, Fernando Pedro Schneider, Jan Di Mitri, Daniele Drachsler, Hendrik DIPF - Leibniz Institute for Research and Information in Education Rostocker Str. 6 Frankfurt am Main60323 Germany Faculty of Computer Science Goethe University Robert-Mayer Str. 11-15 Frankfurt am Main60325 Germany

ISBN: (纸本)9789897587467

Athletes need to train both physical and mental abilities to enhance performance. While technologies such as sensors and actuators have mainly focused on physical training, few solutions support mental training. Virtual reality (VR) technology and cognitive behavioral therapy (CBT) open new possibilities for developing applications targeting this type of training for athletes. We conducted a formative study to investigate the user experience of integrating CBT techniques into VR technology, focusing on two essential mental factors of running: strategy and motivation. To address these factors, we developed a CBT-influenced VR application. We tested the user experience of our VR application with 25 runners. the results revealed that runners had a positive and valuable experience with the VR application and its associated CBT elements. the perceived importance of race strategy was a key predictor of user experience assessments. this study suggests that VR solutions influenced by CBT are a plausible approach to developing suitable mental training tools in sports. Copyright © 2025 by SCITEPRESS - science and technology Publications, Lda.

关键词： Virtual reality

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RobNODDI: Robust NODDI Parameter Estimation with Adaptive Sampling Under Continuous Representation

RobNODDI: Robust NODDI Parameter Estimation with Adaptive S...

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15th International Workshop on Computational Diffusion MRI, CDMRI 2024, held in conjunction with 27th International conference on Medical Image Computing and computer-Assisted Intervention, MICCAI 2024

作者： Xiao, Taohui Cheng, Jian Fan, Wenxin Yang, Jing Li, Cheng Dong, Enqing Wang, Shanshan School of Mechanical Electrical and Information Engineering Shandong University Weihai264209 China State Key Laboratory of Software Development Environment Beihang University Beijing China Key Laboratory of Data Science and Intelligent Computing Institute of International Innovation Beihang University Zhejiang Hangzhou China Paul C. Lauterbur Research Center for Biomedical Imaging Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Guangdong Shenzhen China Peng Cheng Laboratory Guangdong Shenzhen China

ISBN: (纸本)9783031869198

Neurite Orientation Dispersion and Density Imaging (NODDI) is an important imaging technology used to evaluate the microstructure of brain tissue, which is of great significance for the discovery and treatment of various neurological diseases. Current deep learning-based methods perform parameter estimation through diffusion magnetic resonance imaging (dMRI) with a small number of diffusion gradients. these methods speed up parameter estimation and improve accuracy. However, the diffusion directions used by most existing deep learning models during testing needs to be strictly consistent with the diffusion directions during training. this results in poor generalization and robustness of deep learning models in dMRI parameter estimation. In this work, we first verify that the parameter estimation performance of current mainstream methods will significantly decrease when the testing diffusion directions and the training diffusion directions are inconsistent. A robust NODDI parameter estimation method with adaptive sampling under continuous representation (RobNODDI) is proposed. Furthermore, long short-term memory (LSTM) units and fully connected layers are selected to learn continuous representation signals. To this end, we use a total of 100 subjects to conduct experiments based on the Human Connectome Project (HCP) dataset, of which 60 are used for training, 20 are used for validation, and 20 are used for testing. the test results indicate that RobNODDI improves the generalization performance and robustness of the deep learning model, enhancing the stability and flexibility of deep learning NODDI parameter estimatimation applications. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Deep learning

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Enhancing Scalability: A Complete Tree Sharding Architecture Towards IoT 18th

Enhancing Scalability: A Complete Tree Sharding Architecture...

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18th International conference on Wireless Artificial Intelligent Computing Systems and Applications, WASA 2024

作者： Zhang, Luyi Wang, Yujue Ding, Yong Liang, Hai Yang, Changsong Zheng, Haibin Guangxi Key Laboratory of Cryptography and Information Security School of Computer Science and Information Security Guilin University of Electronic Technology Guilin541004 China Hangzhou Innovation Institute of Beihang University Hangzhou310052 China Institute of Cyberspace Technology HKCT Institute for Higher Education Hong Kong Guangdong Provincial Key Laboratory of Novel Security Intelligence Technologies Shenzhen518055 China Guangxi Engineering Research Center of Industrial Internet Security and Blockchain Guilin University of Electronic Technology Guilin541004 China

ISBN: (纸本)9783031714665

With the rapid proliferation of integration of blockchain and IoT, traditional centralized architectures and single-node processing have been effectively solved, but scalability of blockchain have become inadequate to meet the demands of high-load environments. Sharded blockchain brings an innovative approach to tackle the scalability of blockchain and has the potential to greatly enhance the performance of IoT based on blockchain. this paper introduces CTS towards IoT – complete tree sharding that overlaps shards to enable a little shards to retain records of other shards, facilitating the validation of cross-shard transactions as intra-shard transactions. A tree sharding consensus mechanism that relies on the complete tree architecture is proposed to ensure consistency among interconnected shards during the execution of cross-shard transactions. A prototype for CTS towards IoT is simulated and the findings demonstrate that CTS can enhance transaction throughput by a factor of 1.3 when compared to state of the art layered sharding systems observed in a system consisting of 15 shards and 1500 nodes. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Distributed ledger

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Reliability-Enhanced Microservice Deployment 18th

Reliability-Enhanced Microservice Deployment

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18th International conference on Wireless Artificial Intelligent Computing Systems and Applications, WASA 2024

作者： Shi, You Yang, Yuye Yi, Changyan Wang, Junyi College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing China Key Lab of Cognitive Radio and Information Processing Ministry of Education Guilin University of Electronic Technology Guilin China

ISBN: (纸本)9783031714665

Container-based microservice provisioning, with its elasticity in terms of the layered structure, enables the sharing of common layers among different edge computing tasks, both within and across edge servers (ES). However, due to the potential hardware breakdowns, each ES may prone to failures, affecting its lifetime (i.e., the time-length that an ES works continuously without interruptions), and in turn leading to the collapse of their hosted/provided microservices or the other ESs’ microservices requesting common layers from it. Obviously, if such an issue cannot be well addressed, the reliability of microservices in serving corresponding tasks may be severely reduced. In this paper, we study the microservice deployment optimization with layer sharing for maximizing the system-wide reliability while satisfying all tasks’ delay requirements. Considering dynamic task generations and the asynchronization of various decision variables with different triggers, we design an online optimization algorithm by leveraging an improved Lyapunov technique integrating randomized rounding and Lagrangian method, which iteratively solves the problem over different timescales. theoretical analyses and simulations evaluate the performance of the proposed solution, and show its superiority over counterparts. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Lagrange multipliers

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